{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(729)\n",
    "num_observations = 500\n",
    "lr1 = 1e-4\n",
    "MAX_STEP = 300\n",
    "threshold = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "class perception():\n",
    "    def __init__(self, lr, dataset):\n",
    "        self.lr = lr\n",
    "\n",
    "        # 分割数据集\n",
    "        x, y = dataset\n",
    "        self.x_train, self.x_test, self.y_train, self.y_test = train_test_split(\n",
    "            x, y, test_size=0.1, shuffle=True)\n",
    "        self.y_train = self.y_train.reshape(1, -1)\n",
    "        self.y_test = self.y_test.reshape(1, -1)\n",
    "\n",
    "        # 第一列插入 1\n",
    "        self.X_train = np.hstack(\n",
    "            (np.ones((self.x_train.shape[0], 1)), self.x_train))\n",
    "        self.X_test = np.hstack(\n",
    "            (np.ones((self.x_test.shape[0], 1)), self.x_test))\n",
    "\n",
    "        # 权重系数\n",
    "        self.w = np.random.rand(self.X_train.shape[1]).reshape(\n",
    "            (1, self.X_train.shape[1]))\n",
    "\n",
    "        # 记录 loss\n",
    "        self.loss_history = []\n",
    "\n",
    "    def fit(self):\n",
    "        count = 0\n",
    "        w_0 = self.w\n",
    "        while count < MAX_STEP:\n",
    "            for i in range(self.X_train.shape[0]):\n",
    "                pred = self.w.dot(self.X_train[i].T)\n",
    "                self.w = self.w+self.lr * \\\n",
    "                    (self.y_train[0][i]-(pred>threshold))*self.X_train[i]\n",
    "            count = count + 1\n",
    "            self.loss_history.append(\n",
    "                self.loss(self.w.dot(self.X_train[i].T)))\n",
    "\n",
    "            if count % 10 == 1:\n",
    "                P, R, acc = self.mertix(self.X_train, self.y_train)\n",
    "                print(\"Precise: %.2f%%, Recall: %.2f%%, acc: %.2f%%\" %\n",
    "                      (100*P, 100*R, 100*acc))\n",
    "\n",
    "                show_clf(self.X_train, self.y_train, self.w, w_0)\n",
    "                w_0 = self.w\n",
    "\n",
    "        P, R, acc = self.mertix(self.X_test, self.y_test)\n",
    "        print(\"TEST:\\nPrecise: %.2f%%, Recall: %.2f%%, acc: %.2f%%\" %\n",
    "              (100*P, 100*R, 100*acc))\n",
    "        show_loss(self.loss_history)\n",
    "\n",
    "    def mertix(self, X, Y):\n",
    "        \"\"\"P、R、acc\n",
    "        Args:\n",
    "            pred: output label of model\n",
    "        \"\"\"\n",
    "        pred = self.w.dot(X.T) > threshold\n",
    "\n",
    "        # P = TP / (TP + FP)\n",
    "        index_ = pred == 1\n",
    "        TP = (Y[index_] == 1).sum()\n",
    "        P = TP / index_.sum()\n",
    "\n",
    "        # R = TP / (TP + FN)\n",
    "        R = TP / Y.sum()\n",
    "\n",
    "        # acc = (TP + TN) / (TP + FP + TN + FN)\n",
    "        acc = ((pred == Y).sum())/pred.shape[1]\n",
    "\n",
    "        return P, R, acc\n",
    "\n",
    "    def loss(self, pred):\n",
    "        pred = pred > threshold\n",
    "        return 0.5*np.average(pow(pred-self.y_train, 2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dataset():\n",
    "    # 生成两个二元正态分布矩阵作为两类样本，各500个\n",
    "    x1 = np.random.multivariate_normal(\n",
    "        [0, 0], [[1, .75], [.75, 1]], num_observations)\n",
    "    x2 = np.random.multivariate_normal(\n",
    "        [1, 4], [[1, .75], [.75, 1]], num_observations)\n",
    "\n",
    "    X = np.vstack((x1, x2)).astype(np.float32)\n",
    "    Y = np.hstack((np.zeros(num_observations), np.ones(num_observations)))\n",
    "    return X, Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_clf(x, y, w, w_0):\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.scatter(x[:, 1], x[:, 2], c=y, alpha=0.9, edgecolors='black')\n",
    "\n",
    "    xmin, xmax = x[:, 1].min()-1, x[:, 1].max()+1\n",
    "    xx = np.linspace(xmin, xmax, num_observations)\n",
    "    y1 = (threshold - w[:, 0]-xx*w[:, 1]) / w[:, 2]\n",
    "    y2 = (threshold - w_0[:, 0]-xx*w_0[:, 1]) / w_0[:, 2]\n",
    "    plt.plot(xx, y1, label='w')\n",
    "    plt.plot(xx, y2, label=\"w_0\")\n",
    "    display.clear_output(wait=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_loss(loss):\n",
    "    xx = range(MAX_STEP)\n",
    "    yy = loss\n",
    "    plt.plot(xx, yy)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TEST:\n",
      "Precise: 100.00%, Recall: 100.00%, acc: 100.00%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    clf = perception(lr1, dataset())\n",
    "    clf.fit()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "7b4b4feff2f24a0f0a34464dbe537a36fda679851528fb8735cb41fa49dffb2d"
  },
  "kernelspec": {
   "display_name": "Python 3.8.8 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
